apply custom-made function to column pairs and create summary table - r

Question

I have data with ratings on many parameters by two different raters; here are shown just a snippet of ratings on three same-prefix parameters (e.g. DH and DH_ptak):
df <- structure(list(DH = c(0, 1, NA, NA, 1, 1, 1, 1, 1, 1),
DH_ptak = c(0, 1, 1, 1, 1, 1, 1, 1, 1, 1),
SZ = c(1, 1, NA, NA, NA, 0, 1, 0, 1, 1),
SZ_ptak = c(1, 1, NA, NA, NA, 1, 0, NA, 1, 1),
RM = c(0, 1, 1, NA, NA, NA, 0, NA, 1, NA),
RM_ptak = c(0, 1, 1, 1, 1, NA, 0, 1, NA, 1)),
row.names = c(NA, 10L), class = "data.frame")
For each parameter I want to compare the two ratings columns. I use this function to find different ratings:
compare_fun <- function(c1, c2){
case_when(is.na(c1) & is.na(c2) ~ 0,
is.na(c1) | is.na(c2) ~ 1,
c1 != c2 ~ 1,
TRUE ~ 0)
}
I can use this function to sum the differences and compute an agreement percentage agree_pct:
library(dplyr)
df %>%
mutate(diff = compare_fun(DH, DH_ptak)) %>%
summarise(sum = sum(diff),
agree_pct = (nrow(df)-sum)/nrow(df)*100)
sum agree_pct
1 2 80
The problem is that I have multiple parameters. How can I compute for all ratings-column pairs the respective sum and agree_pct in one go, ideally, to obtain a table like this:
sum agree_pct
DH 2 80
SZ 3 70
RM 5 50

Answer 1

This is what I would do. It mostly involves pivoting the data a few times. First I make a column from row names so that I can use this to keep all the rows straight, then I go from wide to long with pivot_longer. I separate the column names to delineate between the two reviewers and assign them the names "grp1" and "grp2". Then I pivot_wider so that you have 2 columns, one for each reviewer. Lastly I apply your function across all the data, group by the variable of interest and summarize the data.
library(tidyverse)
df %>%
rownames_to_column("col") %>%
pivot_longer( -col) %>%
separate(name, into = c("var", "tmp"), sep = "_") %>%
mutate(grp = ifelse(is.na(tmp), "grp1", "grp2")) %>%
select(col, var, value, grp) %>%
pivot_wider(names_from = grp, values_from = value) %>%
mutate(diff = compare_fun(grp1, grp2)) %>%
group_by(var) %>%
summarise(sum = sum(diff),
agree_pct = (nrow(df)-sum)/nrow(df)*100)
#> # A tibble: 3 x 3
#> var sum agree_pct
#> <chr> <dbl> <dbl>
#> 1 DH 2 80
#> 2 RM 5 50
#> 3 SZ 3 70